The MistralEmbedder class is used to embed text data into vectors using the Mistral API. Get your key from here.

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Usage

from phi.agent import Agent, AgentKnowledge
from phi.vectordb.pgvector import PgVector
from phi.embedder.mistral import MistralEmbedder

# Create knowledge base
knowledge_base=AgentKnowledge(
    vector_db=PgVector(
        db_url=db_url,
        table_name=embeddings_table,
        embedder=MistralEmbedder(api_key=***),
    ),
    # 2 references are added to the prompt
    num_documents=2,
),

# Add information to the knowledge base
knowledge_base.load_text("The sky is blue")

# Add the knowledge base to the Agent
agent = Agent(knowledge_base=knowledge_base)

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Params

ParameterTypeDefaultDescription
modelstr"mistral-embed"The name of the model used for generating embeddings.
dimensionsint1024The dimensionality of the embeddings generated by the model.
request_paramsOptional[Dict[str, Any]]-Additional parameters to include in the API request. Optional.
api_keystr-The API key used for authenticating requests.
endpointstr-The endpoint URL for the API requests.
max_retriesOptional[int]-The maximum number of retries for API requests. Optional.
timeoutOptional[int]-The timeout duration for API requests. Optional.
client_paramsOptional[Dict[str, Any]]-Additional parameters for configuring the API client. Optional.
mistral_clientOptional[MistralClient]-An instance of the MistralClient to use for making API requests. Optional.

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